SOTAVerified

Image Classification

Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.

Source: Metamorphic Testing for Object Detection Systems

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5951–5975 of 10420 papers

TitleStatusHype
L_2BN: Enhancing Batch Normalization by Equalizing the L_2 Norms of Features—0
LightGBM robust optimization algorithm based on topological data analysis—0
Differentiable Architecture Compression—0
Light Lies: Optical Adversarial Attack—0
Graph-propagation based Correlation Learning for Weakly Supervised Fine-grained Image Classification—0
Lightweight Adaptive Feature De-drifting for Compressed Image Classification—0
Graph Neural Networks for UnsupervisedDomain Adaptation of Histopathological ImageAnalytics—0
A Hybrid Approach with Optimization and Metric-based Meta-Learner for Few-Shot Learning—0
Graph Neural Networks for Image Classification and Reinforcement Learning using Graph representations—0
Counterfactual Influence as a Distributional Quantity—0
A Transformer-in-Transformer Network Utilizing Knowledge Distillation for Image Recognition—0
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification—0
Linear Context Transform Block—0
Linearised Laplace Inference in Networks with Normalisation Layers and the Neural g-Prior—0
Differentiable Search for Finding Optimal Quantization Strategy—0
Linear Spatial Pyramid Matching Using Non-convex and non-negative Sparse Coding for Image Classification—0
Linked Adapters: Linking Past and Future to Present for Effective Continual Learning—0
Linking ImageNet WordNet Synsets with Wikidata—0
Medical Image Segmentation Using a U-Net type of Architecture—0
Lipschitz-Bounded Equilibrium Networks—0
MedKAN: An Advanced Kolmogorov-Arnold Network for Medical Image Classification—0
Lipschitz Bounded Equilibrium Networks—0
Differentially Private Federated Learning via Inexact ADMM—0
LiteDenseNet: A Lightweight Network for Hyperspectral Image Classification—0
Membership Inference Attacks fueled by Few-Short Learning to detect privacy leakage tackling data integrity—0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10RevCol-HTop 1 Accuracy90—Unverified